Supabase vs Redis
Supabase MCP and Redis MCP both put a data layer under an agent's control, but they address completely different storage roles. Supabase is a Postgres BaaS: its server uses execute_sql for queries, apply_migration for DDL, get_logs and get_advisors for operational visibility, deploy_edge_function for serverless compute, and a branching workflow via create_branch, merge_branch, and rebase_branch. Redis is an in-memory data-structure store: its server exposes set, get, hset, hgetall for strings and hashes; lpush, rpush, lpop, rpop for lists; sadd, smembers for sets; zadd, zrange for sorted sets; xadd, xreadgroup, xack for streams with consumer groups; publish and subscribe for pub/sub; json_set and json_get for JSON documents; and create_vector_index_hash, vector_search_hash, and hybrid_search for HNSW vector similarity search. One manages a relational application backend; the other operates fast, heterogeneous in-memory structures.
How they compare
| Dimension | Supabase | Redis |
|---|---|---|
| Storage model | Relational Postgres: tables with typed columns, foreign keys, and SQL queries. Data persists to disk and survives restarts. | In-memory data structures: strings, hashes, lists, sets, sorted sets, streams, JSON, and vector indexes. Speed is the core property; persistence is optional and configurable. |
| Tool breadth and focus | Platform-wide: SQL, migrations, branches, edge functions, storage buckets, logs by service, security advisors, project/org management, TypeScript codegen, and documentation search. | Data-structure-wide: one tool per operation across eight data types, plus pub/sub, vector index management, key scanning via scan_keys, and server introspection via info and dbsize. |
| Vector and similarity search | No vector tooling in the current MCP server surface. The underlying Postgres database supports pgvector, but the MCP server does not expose dedicated vector index or search tools. | Built-in: create_vector_index_hash creates an HNSW index on hash fields; vector_search_hash runs KNN similarity search; hybrid_search combines a filter expression with KNN. |
| Messaging and event streaming | get_logs retrieves logs for services including realtime; no pub/sub or stream tools directly in the MCP surface. | Full streaming and messaging: xadd/xreadgroup/xack manage streams with consumer groups; publish/subscribe/psubscribe handle pub/sub channels. |
| Best-fit task | Developing and operating a Postgres application backend: schema migrations, edge functions, storage, and platform management all from one server. | Managing fast in-memory data: caches, queues, leaderboards, pub/sub event buses, stream processing, and vector similarity search for embedding lookups. |
Verdict
These two servers rarely compete directly because the data problems they address are different. Supabase MCP is a lifecycle tool for a Postgres-backed application: it is at its best when an agent is building, migrating, or debugging a product. Redis MCP is a data-structure operator: it shines when the agent manages caches, queues, streams, or vector indexes in a fast in-memory store. In production systems they are often complementary, with Redis fronting Supabase's Postgres for caching or embedding retrieval. Choose Supabase MCP when the task is relational application development; choose Redis MCP when the task is in-memory data operations or vector search.
FAQ
- Can Redis MCP manage a Postgres schema or run SQL?
- No. Redis MCP operates on Redis data structures: strings, hashes, lists, sets, sorted sets, streams, JSON, and vector indexes. For SQL queries, schema migrations, and Postgres platform management, Supabase MCP is the appropriate server.
- Which server supports vector search?
- Redis MCP includes create_vector_index_hash, vector_search_hash, and hybrid_search for HNSW vector similarity over hash fields. The Supabase MCP server does not expose dedicated vector index or search tools, even though the underlying Postgres supports pgvector.